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Sequence based approaches do not require prior knowledge of the transcriptome and are therefore useful for discovery and annotation of novel transcripts as well as for analysis of poorly annotated genomes.
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We selected RED, as this clustering-free approach has both recently been applied to P. falciparum and shown to detect motifs that clustering based approaches did not.
Conversely, the wear factor based approach did not predict such differences.
Compared with the NURBS method, this neural network based approach does not need the derivation of complex equation provided that a limited number of offset points are obtained, and its accuracy can meet general engineering needs.
In contrast, our end-sequence based approach does not rely on a priori knowledge of the cpDNA sequence.
As a screen of parameter values becomes necessary in such a scenario, the Monte-carlo based approach doesn't prove to be efficient, as it generally takes longer time to numerically simulate the process and satisfy the imposed conditions.
Thus, standard corpus-based approaches do not work.
However, existing filter-based approaches do not consider effective filter propagation and management.
Compared with the structure-based approaches, ligand-based approaches do not rely on the complete knowledge of ligand-target interaction mechanisms and requires relatively low computational cost.
Because of this lack of prior knowledge, standard likelihood theory or extensions such as generalized likelihood ratios or invariance-based approaches do not apply.
However, ROI-based approaches do not provide accurate distortion estimation, and ROI determination may be difficult for most videos, especially for videos with natural scenes.
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